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Tongue Color Analysis for Medical Application
An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659485/ https://www.ncbi.nlm.nih.gov/pubmed/23737824 http://dx.doi.org/10.1155/2013/264742 |
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author | Zhang, Bob Wang, Xingzheng You, Jane Zhang, David |
author_facet | Zhang, Bob Wang, Xingzheng You, Jane Zhang, David |
author_sort | Zhang, Bob |
collection | PubMed |
description | An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color. |
format | Online Article Text |
id | pubmed-3659485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36594852013-06-04 Tongue Color Analysis for Medical Application Zhang, Bob Wang, Xingzheng You, Jane Zhang, David Evid Based Complement Alternat Med Research Article An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color. Hindawi Publishing Corporation 2013 2013-04-22 /pmc/articles/PMC3659485/ /pubmed/23737824 http://dx.doi.org/10.1155/2013/264742 Text en Copyright © 2013 Bob Zhang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Bob Wang, Xingzheng You, Jane Zhang, David Tongue Color Analysis for Medical Application |
title | Tongue Color Analysis for Medical Application |
title_full | Tongue Color Analysis for Medical Application |
title_fullStr | Tongue Color Analysis for Medical Application |
title_full_unstemmed | Tongue Color Analysis for Medical Application |
title_short | Tongue Color Analysis for Medical Application |
title_sort | tongue color analysis for medical application |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659485/ https://www.ncbi.nlm.nih.gov/pubmed/23737824 http://dx.doi.org/10.1155/2013/264742 |
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